Open Access Highly Accessed Research article

Age-period-cohort analysis for trends in body mass index in Ireland

Tao Jiang1*, Mark S Gilthorpe1, Frances Shiely2, Janas M Harrington2, Ivan J Perry2, Cecily C Kelleher3 and Yu-Kang Tu4

Author Affiliations

1 Division of Epidemiology & Biostatistics, School of Medicine, University of Leeds, Room 8.49, Level 8, Worsley Building, Leeds LS2 9JT, UK

2 University College Cork, Cork, Republic of Ireland

3 School of Public Health, Physiotherapy and Population Science, University College Dublin, Dublin, Republic of Ireland

4 Institute of Epidemiology & Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan

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BMC Public Health 2013, 13:889  doi:10.1186/1471-2458-13-889

Published: 25 September 2013



Obesity is a growing problem worldwide and can often result in a variety of negative health outcomes. In this study we aim to apply partial least squares (PLS) methodology to estimate the separate effects of age, period and cohort on the trends in obesity as measured by body mass index (BMI).


Using PLS we will obtain gender specific linear effects of age, period and cohort on obesity. We also explore and model nonlinear relationships of BMI with age, period and cohort. We analysed the results from 7,796 men and 10,220 women collected through the SLAN (Surveys of Lifestyle, attitudes and Nutrition) in Ireland in the years 1998, 2002 and 2007.


PLS analysis revealed a positive period effect over the years. Additionally, men born later tended to have lower BMI (−0.026 kg·m-2 yr-1, 95% CI: -0.030 to −0.024) and older men had in general higher BMI (0.029 kg·m-2 yr-1, 95% CI: 0.026 to 0.033). Similarly for women, those born later had lower BMI (−0.025 kg·m-2 yr-1, 95% CI: -0.029 to −0.022) and older women in general had higher BMI (0.029 kg·m-2 yr-1, 95% CI: 0.025 to 0.033). Nonlinear analyses revealed that BMI has a substantial curvilinear relationship with age, though less so with birth cohort.


We notice a generally positive age and period effect but a slightly negative cohort effect. Knowing this, we have a better understanding of the different risk groups which allows for effective public intervention measures to be designed and targeted for these specific population subgroups.

Obesity; Age-period-cohort; Partial least squares